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Step-by-Step Guide to Reading Different Files in Python

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 In the world of data science, automation, and general programming, working with files is unavoidable. Whether you’re dealing with CSV reports, JSON APIs, Excel sheets, or text logs, Python provides rich and easy-to-use libraries for reading different file formats. In this guide, we’ll explore how to read different files in Python , with code examples and best practices. 1. Reading Text Files ( .txt ) Text files are the simplest form of files. Python’s built-in open() function handles them effortlessly. Example: # Open and read a text file with open ( "sample.txt" , "r" ) as file: content = file.read() print (content) Explanation: "r" mode means read . with open() automatically closes the file when done. Best Practice: Always use with to handle files to avoid memory leaks. 2. Reading CSV Files ( .csv ) CSV files are widely used for storing tabular data. Python has a built-in csv module and a powerful pandas library. Using cs...

6 Advantages of Columnar Databases over Traditional RDBMS

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In traditional RDBMS, when a data source is accessed by multi users at single time, then database will go into deadlock state. One of the advantages of a columnar model is that if two or more users want to use a different subset of columns, they do not have to lock out each other.         (Superior benefits for NoSQL Jobs) This design is made easier because of a disk storage method known as RAID (redundant array of independent disks, originally redundant array of inexpensive disks), which combines multiple disk drives into a logical unit. Data is stored in several patterns called levels that have different amounts of redundancy. The idea of the redundancy is that when one drive fails, the other drives can take over. When a replacement disk drive in put in the array, the data is replicated from the other disks in the array and the system is restored. The following are the various levels of RAID: RAID 0 (block-level striping without parity or mirroring) ...